1. Field
The present disclosure relates to a brain-computer interface apparatus for supporting the rehabilitation of stroke patients with a probe for recording a neuronal activity signal, an evaluation unit for analysis of the activity signal, and an effector.
2. Background
In recent years, interest in BCI systems (brain-computer interface) has strongly increased. Generally, this refers to systems that enable the direct control of technical devices by analysis of the brain activity, or that conversely effect a stimulation of the brain. The term is often used in a somewhat narrower sense for medical motor applications, where at least a part of the autonomy lost by a health impairment is restored to patients by prostheses or a cursor control.
Various methods are known to measure the neuronal activity of a patient. There is an inverse interdependence of the resolution of the recorded activity information and the degree of having to invasively intervene with the body. At one end of the scale are EEG (electroencephalography) or MEG (magnetoencephalography) that use only external electrodes, yet also allow for but a relatively coarse spatial resolution. At the other end of the scale action potentials of individual cells (SUA, single unit activity) or local field potentials (LFP) may be recorded with electrodes penetrating the brain tissue. This allows for a high spatial resolution, but causes a virtually permanent open wound with the corresponding symptoms and risks of infection. Moreover, it is difficult to achieve a recording with long term stability. An intermediate solution is offered by ECoG electrodes (electrocorticography) that are arranged directly on the surface of the brain below the skull. They are long-time compatible and provide significantly improved spatially resolved neuronal activity information as compared to EEG or MEG. Such a BCI is presented in U.S. Pat. No. 7,120,486 B2. The paper of Tonio Ball and others, “Differential representation of arm movement direction in relation to cortical anatomy and function,” J. Neural Eng. 2009:6 No. 016 006 studies which frequency ranges of an ECoG probe show a highly movement specific decoding power.
The vast majority of the previously discussed medical applications are intended for patients with motor impairments where an organic healing is no longer possible, although the cortex, or at least the motor cortex as the area that is essential for the control of voluntary movements, is at least partially intact, whereas the nerve connections to the musculature are interrupted. The BCI system is therefore to be regarded as a pure neural prosthesis that replaces a natural function of the body without healing it.
As long as a residual mobility is preserved, the healing can be supported by specific physiotherapy. As soon as a body part is completely paralyzed, the only attempt left for the traditional medicine is to passively and externally move the affected body part. But this is usually not sufficient to initiate a neuronal reorganization in the brain areas affected by the stroke, which might improve the motor impairment.
Ethan Buch and others in “Think to Move: a Neuromagnetic Brain-Computer Interface (BCI) System for Chronic Stroke”, Stroke 2008:39, pages 910-917, have taught stroke patients to open or close a paralyzed hand with the aid of an orthosis (orthopedic prosthesis). An orthosis supports a movement of a body part, but does not replace the body part. For the control of the orthosis, patients were to achieve, by relaxing, a synchronization or desynchronization, i.e. a strengthening or weakening, of a neuronal activity analyzed by MEG, which is then translated into the corresponding opening state of the orthosis. Although the control could be successfully learned, no improvement of the motor impairment was achieved. A further disadvantage of this method is that the patients had to learn generation of artificial brain activity and not necessarily brain activity specific for the movement of the paralyzed body part. In this case, mu activity in the band of 8-12 Hz was evaluated, that can like beta activity in the band of 18-26 Hz be primarily detected above the somatosensoy an motor cortices. Movement or movement preparation are often associated with a reduction of mu and beta rhythms, while after a movement or during relaxation a temporary strengthening of rhythms usually takes place. Subjects can learn to purposefully influence these activities, but this takes a lot of concentration and is also very time consuming Moreover, mu and beta activity only indicates movement as such, but is not suitable to decode or predict specific movements or movement intentions.
Janis Daly and Jonathan Wolpaw in “Brain-computer interfaces in neurological rehabilitation,” Lancet Neurol. 2008:7, pages 1032-1043, discuss the idea to directly train specific activity patterns without use of an orthosis. However, the paper is silent on the issue of what activity patterns these might be and how they could be identified from the recorded neuronal activity.
Another idea from Nick Ward, “The Neural Substrates of Motor Recovery After Focal Damage to the Central Nervous System”, Arch Phys. Med. Rehabil. 2006:87 Suppl 2, Pages S30-S35 is to electrically stimulate the motor cortex. This is not related to an identification of specific neuronal activity of the patient, though, but only with traditional physiotherapy. A BCI system for stroke rehabilitation is thus not addressed in any way.
It is known that electrical stimulation can generally promote healing of damaged brain tissue. However, there is no evidence that a treatment of motor impairments following a stroke by electrical stimulation alone shows any success. This isolated stimulation was, for example, tried out in a study of Northstar Neuroscience Inc. and did not lead to the desired results (see, for example, at http://www.reuters.com/article/idUSWNAS705820080122).